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Comparative Study of Forecasting Algorithms for Energy Data · Nishat Fariha Rimi 20th May, 2019...
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Albert-Ludwigs-Universität Freiburg
Master Thesis Presentation by
Nishat Fariha Rimi
20th May, 2019
Comparative Study of Forecasting
Algorithms for Energy Data
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Motivation
❑ Wind and solar energy varies
❑ What is produced must be used
Benefits of energy demand forecasting–
❑ Balances supply and demand
❑ Prevents energy waste
❑ Reduces operation cost
Comparative analysis of forecasting
methods depending on
❑ Time scale
❑ Dataset type and sample size
20.05.2019 Computer Science Department, Albert-Ludwigs-Universität Freiburg 2
Source: Science direct. smart grid and solar energy
Source: https://www.iass-potsdam.de
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Outline
Overview
- Implemented forecasting methods
- Considered forecasting scenarios
Methodology
- Methods
- Forecasting toolbox
Performance analysis
- Performance comparison
Conclusion and Future Work
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Literature Review
Method Selection
Implementation
Performance Analysis
Findings
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Goal
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➢ Create: Structure of a forecasting toolbox
➢ Compare: Methods performance according to datasets and
forecasting scenarios
Computer Science Department, Albert-Ludwigs-Universität Freiburg
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Selected Forecasting Approaches
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Forecasting Approaches
Statistical Approaches
Autoregression
Autoregressive moving-average
(ARMA)
Autoregressive integrated moving
average
(ARIMA)
Smoothing
Exponential smoothing
(ES)
Holt-Winters method (HW)
Machine Learning Approaches
Classical Machine Learning
K-nearest neighbor
(KNN)
Random forest (RF)
Deep Learning
Artificial neural network
(ANN)
Recurrent neural network
(RNN)
Computer Science Department, Albert-Ludwigs-Universität Freiburg
Lahouar and J. Ben Hadj Slama Energy Conversion and Management, vol. 103, pp. 1040–1051, 2015.
R. J. Hyndman and G. Athanasopoulos, Forecasting : Principles and Practice. OTexts, 2018.
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Considered Scenarios
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Performance comparison according to different forecasting aspects.
50 100 200 400 800 1600 2200Training
Sample Size
PV generation Electrical loadDatasets
Forecasting
horizon
Time scale Considered
time scale
Forecasting
sample size
Very short-term 5 min- 1 h 1 h 1
Short-term 1 h- 24 h 1 d 24
Medium-term 24 h- weeks 1 w 24*7
Long-term month-years 1 m
3 m
24*7*4
24*7*4*3
Computer Science Department, Albert-Ludwigs-Universität Freiburg
P. Kuo, Energies, vol. 11, January, pp. 1–13, 2018
J. W. Taylor, International Journal of Forecasting, vol. 24, no. 4, pp. 645–658, 2008
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Statistical Approaches
Depend on the past values of endogenous variable for forecasting
ARMA (p,q):
▪ Combination of AR 𝑝 and MA 𝑞 models for stationary time series
ARIMA (p,d,q):
▪ Transform the non-stationary data into stationary by differencing
ES (𝜶):
▪ Assign exponentially decreasing weights for past observations
HW (α, β, γ):
▪ Design to capture trend and seasonality
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C.-M. Lee et al. Expert Systems with Applications, vol. 38, pp. 5902–5911, 2011.
R. Weron, Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. 2006
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Machine Learning & Deep Learning
Approaches
Exogenous information and endogenous variables used together
RF (n_tree, max_depth):
▪ Constructs multiple decision trees during training
KNN (k):
▪ Searches for a group of k samples nearest based on distance function
ANN (hidden_node, hidden layer, epoch):
▪ Allows data signals to process the output in one way
RNN (hidden_node, hidden layer, epoch):
▪ Use internal state (memory) to process sequences of inputs
20.05.2019 8Computer Science Department, Albert-Ludwigs-Universität Freiburg
C. Xia et al. International Journal of Electrical Power & Energy Systems, vol. 32, pp. 743–750, 2010.
M. Thanh Noi et al. Sensors, vol. 18, no. 1, 2018.
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MethodologyParameter Optimization
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Electrical load time series
PV generation
Electricalload
PV generation
Endogenous
Hour of the day
Diffused Solar radiation
Exogenous
Optimization of hyperparameter:
Train
Execute
Change hyperparameter
Optimized set of hyperparameter
Data
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MethodologyForecasting Toolbox
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....
FilesMoving
Training
Op
tim
ize
d p
ara
me
ter
of
each m
eth
od
Data
pre-processing
Methods
Forecast Evaluate Output
ARMA
ANN
RNN
ARIMA
Testin
g
Accuracy
metrics, Plots
Save
prediction info
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Performance AnalysisStatistical Approaches
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➢ Days Plot – PV Generation
➢ Forecasting sample size- 1
➢ Training sample size- 2200
Computer Science Department, Albert-Ludwigs-Universität Freiburg
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Performance AnalysisMachine Learning Approaches
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➢ Days Plot – PV Generation
➢ Forecasting sample size- 1
➢ Training sample size- 2200
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Performance AnalysisAll methods
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➢ 1 Day Plot – PV Generation
➢ Statistical methods and Machine learning methods
➢ Forecasting sample size- 1
➢ Training sample size- 2200
Computer Science Department, Albert-Ludwigs-Universität Freiburg
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Learning Time Comparison
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Mean training time in seconds
PV generation: (daily forecasting for 100 training sample size)
Electrical load: (daily forecasting and 100 training sample size)
➢ Training time increases gradually with the increase of training sample sizes
HW
0.004
ES
0.006ARMA
0.009
ARIMA
0.11
RF
0.29
ANN
8.33
RNN
32.983
KNN
0.005ES
0.006
KNN
0.005
ARMA
0.034
ES
0.0079
HW
0.25
ARIMA
0.106
RF
0.35
ANN
8.41
RNN
48.27
KNN
0.0084
Computer Science Department, Albert-Ludwigs-Universität Freiburg
PC configuration: Windows 10 computer, with 4 Cores, 8GB of ram, and with 3.4 GHz clock speed.
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Predicting Time Comparison
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Mean predicting time in seconds
PV generation: (monthly forecasting with 2200 training sample size)
Electrical load : (monthly forecasting with 2200 training sample size)
➢ predicting time has increased gradually with the increase of prediction horizons
ARMA
0.004
ES
0.011
HW
0.027
KNN
0.038
RF
0.048
RNN
0.05
ANN
0.06
ARIMA
0.018
ES
0.023
ARIMA
0.02
KNN
0.021
HW
0.031
RF
0.048
ANN
0.06
RNN
0.98
ARMA
0.013
Computer Science Department, Albert-Ludwigs-Universität Freiburg
PC configuration: Windows 10 computer, with 4 Cores, 8GB of ram, and with 3.4 GHz clock speed.
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Performance AnalysisIndicators
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Accuracy Metrices
R value
RMSE
MAE
MAPE
ForecastingTimescale
Hour
Day
Week
Month
3 Months
ARMA
ARIMA
ES
HW
KNN
RF
ANN
RNN
Electrical demand
PV generation
50
100
200
400
800
1600
2200
Training Sample Size
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RMSE Comparison for PV Generation
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✓ARIMA, ARMA
− ESHourly
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✓RF, KNN
✓HW, RNN (1600, 2200)
− ES, ARMA
Daily
MAE Comparison for PV Generation
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RMSE Comparison Summary
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✓ARIMA, ARMA
− ES
✓RF, KNN
✓HW, RNN (1600, 2200)
− ARMA, RNN (small sample)
✓RF, KNN,
✓HW (1600,2200),RNN (2200)
− RNN (small sample)
✓RF, KNN,
✓RNN (1600,2200)
− HW
Hourly
Daily
Weekly
Monthly
✓ARIMA, ARMA
− HW
✓RF, KNN,
✓RNN, HW (>400)
− ARMA, ES
✓Similar to Daily
− Similar to Daily
✓RF, KNN,RNN
✓HW (>200)
− ARMA, ES
Photovoltaic Dataset
Electrical Load Dataset
Computer Science Department, Albert-Ludwigs-Universität Freiburg
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MAE Comparison Summary
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✓ARIMA, ARMA
− ES
✓RF, KNN,
✓HW,RNN (1600, 2200)
− ES, ARMA
✓Similar to Daily
− Similar to Daily
✓RF, KNN
✓RNN (sample size > 800)
− HW
Hourly
Daily
Weekly
Monthly
Photovoltaic Dataset
✓ARIMA, ARMA
− HW
✓RF, KNN,
✓RNN,HW (>400)
− ARMA, ES
✓Similar to Daily
− Similar to Daily
✓RF, KNN,RNN
✓HW (>200)
− ARMA, ES
Electrical Load Dataset
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Conclusion
Comparative analysis of eight forecasting methods –
❑ Prediction horizon
❑ Training sample size
❑ PV generation and electrical load
Hourly
❑ ARMA and ARIMA – optimum choice
❑ Computation time of ARMA < Computation time of ARIMA
Daily, weekly and monthly
❑ RF and KNN
❑ Computation time of KNN < Computation time of RF
HW and RNN - large dependency on sample size
20.05.2019 21Computer Science Department, Albert-Ludwigs-Universität Freiburg
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Future Work
Adding other forecasting approaches e.g.
❑ Support vector regression
❑ Gaussian process regression etc.
Optimizing parameter with dynamic optimization function
❑ Genetic algorithm
Training with –
❑ Datasets of shorter time interval like 15 or 30 minutes
❑ More datasets / applications
20.05.2019 22Computer Science Department, Albert-Ludwigs-Universität Freiburg
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Thank You